Unsupervised Anomaly Detection for IoT-Driven Multivariate Time Series on Moringa Leaf Extraction

Abstract

The extraction of valuable compounds from moringa plants involves complex processes that are highly dependent on various environmental and operational factors. Monitoring these processes using Internet of Things (IoT)-based multivariate time series data presents a unique opportunity for improving efficiency and quality control. Multivariate time series data, characterized by multiple variables recorded over time, provides valuable insights into the behavior, interactions, and dependencies among different components within a system. However, with the increasing complexity and volume of IoT data generated during moringa extraction, the anomaly detection becomes challenging. The objective of this study is to develop a robust and efficient system capable of automatically detecting anomalous patterns in real time, providing early warning signals to operators, and facilitating timely interventions. This paper proposes a novel hybrid unsupervised anomaly detection model combining density-based spatial clustering of applications with noise and k-nearest neighbors for IoT-based multivariate time series data. We conducted extensive experiments on real-world moringa extraction, demonstrating the effectiveness and practicality of our proposed approach. In comparison to other anomaly detection methods, our proposed method has the highest precision value of 0.89, the highest recall value of 0.89, and the highest accuracy value of 0.87. Future research will measure and optimize actuators (relays and motors) from anomaly detection to action. It can also be used with forecasting algorithms to detect anomalies in the coming minutes.